People say Big Data is the difference between driving blind in your business and having a full 360-degree view of your surroundings. But, adopting big data is not only about collecting data. You don’t get a Big Data club card just for changing your old (but still trustworthy) data warehouse into a data lake (or even worse, a data swamp).
Big Data is not only about sheer volume of data. It’s not about making a muscular demonstration of how many petabytes you stored.
To make a Big Data initiative succeed, the trick is to handle widely varied types of data, disparate sources, datasets that aren’t easily linkable, dirty data, and unstructured or semi-structured data.
At least 40% of the C-level and high-ranking executives surveyed in the most recent NewVantage Partners’ Big Data Analytics Survey agree. Only 14.5% are worried about the volume of the data they’re trying to handle.
One OpenText prospect’s Big Data struggle is a perfect example of why the key challenge is not data size but complexity. Recently, OpenText™ Analytics got an inquiry from an airline that needed better insights in order to head off customer losses. This low-cost airline had made a discovery about its loyal customers. Some of them, without explanation, would stop booking flights. These were customers that used to fly with them every month or even every week, but were now disappearing unexpectedly.
The airline’s CIO asked why this was happening. The IT department struggled to push SQL queries against different systems and databases, exploring common scenarios for why customers leave. They examined:
- The booking application, looking for lost customers (or “churners”). Who has purchased flights in previous months but not the most recent month? Which were their last booked flights?
- The customer service ticketing system to find if any of the “churners” found in the booking system had a recent claim. Were any of those claims solved? Closed by the customer? Was there any hint of customer dissatisfaction? What are the most commonly used terms in their communications with the airline – for example, prices? Customer support? Seats? Delays? And what was the tone or sentiment around such terms? Were they calm or angry? Merely irked, or furious and threatening to boycott the airline?
- The database of flight delays, looking for information about the churners’ last bookings. Were there any delays? How long? Were any of these delayed flights cancelled?
Identifying segments of customers who left the company during the last month, whether due to claims unresolved or too many flights delayed or canceled, would be the first step towards winning them back. So at that point, the airline’s IT department’s most important job was to answer the CIO’s question – May I have this list of customers?
The IT staff needed more than a month to get answers to these questions, because the three applications and their databases didn’t share information effectively. First they had to move long lists of customer IDs, booking codes, and flight numbers from one system to another. Then repeat the process when the results weren’t useful. It was a nightmare crafted of disperse data, complex SQL queries, transformation processes, and lots of efforts – and it delivered answers too late for the decision-maker. A new month came with more lost customers.
That’s when the airline realized it needed a more powerful, flexible analytics solution that could effortlessly draw from all its various data sources. Intrigued by the possibilities of OpenText Analytics, it asked us to demonstrate how we could solve its problems.
Using Big Data Analytics, we blended the three disparate data sources. In just 24 hours we were able to answer the questions and OpenText™ Big Data Analytics had worked its magic.
The true value of Big Data is getting answers out of data coming from several diverse sources and different departments. This is the pure 360-degree view of business that everyone is talking about. But without an agile and flexible way to get that view, value is lost in delay.
Analytical repositories that use columnar technologies – i.e., what OpenText Analytics solutions are built on – are there to help answer questions fast when a decision-maker needs answers to business challenges.
